Machine Learning For Cybersecurity Threat Detection And Mitigation
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Machine Learning For Cybersecurity Threat Detection And Mitigation
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Author : Dr. Araddhana Arvind Deshmukh
language : en
Publisher: Xoffencer international book publication house
Release Date : 2024-07-05
Machine Learning For Cybersecurity Threat Detection And Mitigation written by Dr. Araddhana Arvind Deshmukh and has been published by Xoffencer international book publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-05 with Computers categories.
As a result of the increasingly complex structure of today's information systems, there is a growing agreement that Artificial Intelligence (AI) is required in order to keep up with the exponential expansion of big data. Techniques from the field of machine learning (ML), in particular deep learning, are already being used to address a broad range of issues that are encountered in the real world. There are a number of intriguing examples of machine learning's practical triumphs, including machine translation, recommendations for vacations and travel, item identification and monitoring, and even various applications in the healthcare industry. Furthermore, machine learning has shown a great deal of promise in the area of autonomous driving and communication systems, which is why it is rightly considered to be a technical enabler. On the other hand, the civilization of today is more reliant than ever before on information technology systems, even autonomous ones, which are itself abused by malicious actors. In actuality, cybercriminals are always inventing new threats, and, they will have the ability to do significant harm or even kill people due to their capabilities. In order for defensive mechanisms to be able to prevent such events and limit the multiplicity of hazards that might potentially harm both current and future information technology systems, they need to be able to quickly adapt to (i) settings that are continually changing and (ii) threat landscapes that are always developing. It is hard to ignore the use of machine learning in the field of cybersecurity since it is manifestly impossible to address such a dual demand using methodologies that are static and human-defined. It is not surprising that a number of surveys and technical studies have been conducted on the subject of machine learning integration in the field of cybersecurity. Even though there have been a lot of accomplishments in research settings, there has been only a little amount of progress made in creating and integrating machine learning in industrial systems. The vast majority of these solutions are still using 'unsupervised' techniques, mostly for 'anomaly detection,' according to a recent report. This is despite the fact that more than ninety percent of enterprises are presently incorporating AI and ML into their defensive systems.
Handbook Of Research On Machine And Deep Learning Applications For Cyber Security
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Author : Padmavathi Ganapathi
language : en
Publisher: IGI Global, Information Science Reference
Release Date : 2019-07-26
Handbook Of Research On Machine And Deep Learning Applications For Cyber Security written by Padmavathi Ganapathi and has been published by IGI Global, Information Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-26 with Computers categories.
"This book explores the use of machine learning and deep learning applications in the areas of cyber security and cyber-attack handling mechanisms"--
Machine Learning For Cybersecurity
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Author : Abdussalam Elhanashi
language : en
Publisher:
Release Date : 2024-12-12
Machine Learning For Cybersecurity written by Abdussalam Elhanashi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-12 with Mathematics categories.
"Machine Learning for Cybersecurity: Threat Detection and Mitigation" delves into the transformative role of machine learning in addressing contemporary cybersecurity challenges. This reprint provides an in-depth exploration of how advanced techniques such as deep learning, natural language processing, and explainable AI are revolutionizing intrusion detection, anomaly detection, and threat intelligence. With a focus on practical applications, it covers critical topics such as malware analysis, IoT and cloud security, blockchain security, adversarial attacks, and secure data sharing. Through this reprint, readers will gain insights into cutting-edge approaches for vulnerability assessments, authentication, and privacy preservation while exploring frameworks for implementing security-aware AI systems. This comprehensive resource is essential for researchers, practitioners, and policymakers striving to strengthen digital ecosystems. It offers both theoretical insights and actionable solutions, paving the way for innovative cybersecurity strategies to combat an ever-evolving threat landscape.
Hands On Machine Learning For Cybersecurity
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Author : Soma Halder
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-31
Hands On Machine Learning For Cybersecurity written by Soma Halder and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-31 with Computers categories.
Get into the world of smart data security using machine learning algorithms and Python libraries Key FeaturesLearn machine learning algorithms and cybersecurity fundamentalsAutomate your daily workflow by applying use cases to many facets of securityImplement smart machine learning solutions to detect various cybersecurity problemsBook Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learnUse machine learning algorithms with complex datasets to implement cybersecurity conceptsImplement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problemsLearn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDAUnderstand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimesUse TensorFlow in the cybersecurity domain and implement real-world examplesLearn how machine learning and Python can be used in complex cyber issuesWho this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book
Cyber Threat Intelligence
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Author : Ali Dehghantanha
language : en
Publisher: Springer
Release Date : 2018-04-27
Cyber Threat Intelligence written by Ali Dehghantanha and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-27 with Computers categories.
This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the researcher and digital forensics experts, in order to build intelligent automated systems to fight against advanced cybercrimes. The ever increasing number of cyber-attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time, and with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions – this in essence defines cyber threat intelligence notion. However, such intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyze, and interpret cyber-attack campaigns which is covered in this book. This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive reason, learn and act on a wide range of data collected from different cyber security and forensics solutions. This book introduces the notion of cyber threat intelligence and analytics and presents different attempts in utilizing machine learning and data mining techniques to create threat feeds for a range of consumers. Moreover, this book sheds light on existing and emerging trends in the field which could pave the way for future works. The inter-disciplinary nature of this book, makes it suitable for a wide range of audiences with backgrounds in artificial intelligence, cyber security, forensics, big data and data mining, distributed systems and computer networks. This would include industry professionals, advanced-level students and researchers that work within these related fields.
Machine Learning Approaches In Cyber Security Analytics
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Author : Tony Thomas
language : en
Publisher: Springer
Release Date : 2021-01-02
Machine Learning Approaches In Cyber Security Analytics written by Tony Thomas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-02 with Computers categories.
This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks.
Machine Intelligence Applications In Cyber Risk Management
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Author : Almaiah, Mohammed Amin
language : en
Publisher: IGI Global
Release Date : 2024-11-29
Machine Intelligence Applications In Cyber Risk Management written by Almaiah, Mohammed Amin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
In an era where cyber threats are increasingly sophisticated and persistent, the intersection of machine intelligence and cyber-risk management represents a pivotal frontier in the defense against malicious actors. The rapid advancements of artificial intelligence (AI) and machine learning (ML) technologies offer unprecedented capabilities for identifying, analyzing, and mitigating cyber risks. These technologies not only improve the speed and accuracy of identifying potential threats but also enable proactive and adaptive security measures. Machine Intelligence Applications in Cyber-Risk Management explores the diverse applications of machine intelligence in cyber-risk management, providing a comprehensive overview of how AI and ML algorithms are utilized for automated incident response, threat intelligence gathering, and dynamic security postures. It addresses the pressing need for innovative solutions to combat cyber threats and offer insights into the future of cybersecurity, where machine intelligence plays a crucial role in creating resilient and adaptive defense mechanisms. Covering topics such as anomy detection algorithms, malware detection, and wireless sensor networks (WSNs), this book is an excellent resource for cybersecurity professionals, researchers, academicians, security analysts, threat intelligence experts, IT managers, and more.
Machine Learning And Security
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Author : Clarence Chio
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-01-26
Machine Learning And Security written by Clarence Chio and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-26 with Computers categories.
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
Ml And Cybersecurity Ai For Threat Detection And Prevention
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Author : Dr. Araddhana Manisha Arvind Deshmukh
language : en
Publisher: Academic Guru Publishing House
Release Date : 2025-01-06
Ml And Cybersecurity Ai For Threat Detection And Prevention written by Dr. Araddhana Manisha Arvind Deshmukh and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Study Aids categories.
ML and Cybersecurity: Al for Threat Detection and Prevention" is a comprehensive guide that focuses on the critical role of Artificial Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. As cyber threats become increasingly sophisticated and frequent, traditional security methods struggle to provide adequate protection. This book addresses the growing demand for Al-powered solutions that can detect and prevent threats in real-time. The book provides a detailed exploration of various Al and ML techniques used to enhance cybersecurity, including supervised and unsupervised learning, behavioral analysis, and predictive analytics. It explains how Al technologies help identify threats faster and more accurately, reduce human error, and streamline security processes. Moreover, the book highlights practical applications and real-world examples, including Al-powered intrusion detection systems, automated incident response, and enhanced security information systems. Additionally, ethical considerations, privacy concerns, and challenges related to integrating Al with cybersecurity are thoroughly examined.
Ai Driven Cybersecurity And Threat Intelligence
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Author : Iqbal H. Sarker
language : en
Publisher: Springer Nature
Release Date : 2024-04-28
Ai Driven Cybersecurity And Threat Intelligence written by Iqbal H. Sarker and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-28 with Computers categories.
This book explores the dynamics of how AI (Artificial Intelligence) technology intersects with cybersecurity challenges and threat intelligence as they evolve. Integrating AI into cybersecurity not only offers enhanced defense mechanisms, but this book introduces a paradigm shift illustrating how one conceptualize, detect and mitigate cyber threats. An in-depth exploration of AI-driven solutions is presented, including machine learning algorithms, data science modeling, generative AI modeling, threat intelligence frameworks and Explainable AI (XAI) models. As a roadmap or comprehensive guide to leveraging AI/XAI to defend digital ecosystems against evolving cyber threats, this book provides insights, modeling, real-world applications and research issues. Throughout this journey, the authors discover innovation, challenges, and opportunities. It provides a holistic perspective on the transformative role of AI in securing the digital world. Overall, the useof AI can transform the way one detects, responds and defends against threats, by enabling proactive threat detection, rapid response and adaptive defense mechanisms. AI-driven cybersecurity systems excel at analyzing vast datasets rapidly, identifying patterns that indicate malicious activities, detecting threats in real time as well as conducting predictive analytics for proactive solution. Moreover, AI enhances the ability to detect anomalies, predict potential threats, and respond swiftly, preventing risks from escalated. As cyber threats become increasingly diverse and relentless, incorporating AI/XAI into cybersecurity is not just a choice, but a necessity for improving resilience and staying ahead of ever-changing threats. This book targets advanced-level students in computer science as a secondary textbook. Researchers and industry professionals working in various areas, such as Cyber AI, Explainable and Responsible AI, Human-AI Collaboration, Automation and Intelligent Systems, Adaptive and Robust Security Systems, Cybersecurity Data Science and Data-Driven Decision Making will also find this book useful as reference book.